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Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

Showing 13311340 of 9051 papers

TitleStatusHype
Distributed speech separation in spatially unconstrained microphone arraysCode1
Griffin: Towards a Graph-Centric Relational Database Foundation ModelCode1
MoPS: Modular Story Premise Synthesis for Open-Ended Automatic Story GenerationCode1
Mosaic-IT: Free Compositional Data Augmentation Improves Instruction TuningCode1
G-Eval: NLG Evaluation using GPT-4 with Better Human AlignmentCode1
Data Curation Alone Can Stabilize In-context LearningCode1
Distribution-aware Knowledge Prototyping for Non-exemplar Lifelong Person Re-identificationCode1
Multi-head Attention-based Deep Multiple Instance LearningCode1
GPT-FL: Generative Pre-trained Model-Assisted Federated LearningCode1
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problemCode1
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